Name
Abstract | ||
|---|---|---|
Matrix data has arised in many field, especially in the field of image processing and computer vision. In traditional approaches, the original images need to be vectorized to one-dimension vectors, which may destroy the inherent structure of images. A novel geometrical sparse representation (GSR) model with single image is introduced in this paper that solves a model to measure the similarity between the input image and the single dictionary image. Unlike the traditional sparse representation model, the proposed model does not need to vectorize the image, so as to preserve the inherent geometrical structure of the image. We further introduce a binary coding method to preserve the local patterns of the image and enhance the sparsity of the GSR coefficients. Our method is used for face images with variations of structural noise (occlusion, illumination, etc.), extensive experiments show that our method can be competitive with or even superior to the baseline methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.imavis.2021.104212 | Image and Vision Computing |
Keywords | DocType | Volume |
Geometrical structure,Sparse coding,Illumination analysis,Occlusion | Journal | 112 |
ISSN | Citations | PageRank |
0262-8856 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin Tan | 1 | 2 | 2.06 |
| Taiping Zhang | 2 | 14 | 4.60 |
| Linchang Zhao | 3 | 2 | 3.07 |
| Xiaoliu Luo | 4 | 1 | 1.03 |
| Yuan Yan Tang | 5 | 0 | 0.34 |